Amir Toliyat | | College of Staten Island Website

Assistant Professor

Amir Toliyat is an Assistant Professor of Information Systems & Informatics in the Department of Marketing at the Lucille and Jay Chazanoff School of Business, College of Staten Island, CUNY. He earned a Ph.D. in Computer Science from the CUNY Graduate Center. His research focuses on artificial intelligence, machine learning, natural language processing, large language models, Responsible AI, and AI-enabled decision-support systems. He develops and evaluates computational methods, models, and reproducible analytical frameworks for reliable, responsible, and data-driven decision-making across application domains including healthcare, public health, organizational decision-making, cybersecurity, transportation, and other data-intensive settings. His work emphasizes technically substantive contributions in areas such as model evaluation, evidence fidelity, robustness, reproducibility, error analysis, and human-AI decision-making. Before joining CSI, he served as a tenure-track Assistant Professor of Computer Science at Vaughn College and taught at several CUNY colleges as an Adjunct Assistant Professor. His teaching emphasizes hands-on technical work, rigorous interpretation of analytical results, and connecting AI and computing methods to real-world problems.

Degrees

Ph.D. in Computer Science, CUNY Graduate Center

M.Phil. in Computer Science, CUNY Graduate Center

M.S. in Computer Engineering – Artificial Intelligence, IAUM

B.S. in Computer Engineering – Software, Sadjad University

Scholarship and Publications

Selected publications and recognition:

Gorman, C., Toliyat, A., Vatankhah, M., & Fuller, C. (2026). A Protocol-Aware AI Framework for Securing CPDLC Communications. ICNS 2026 - 2nd Place Best Student Paper.
Vatankhah, M., & Toliyat, A. (2025). Deep Learning in Healthcare: Unsupervised Anomaly Detection with Enhanced Scoring Method. ICBRA 2025, 71-76.
Toliyat, A., Filatova, E., & Etemadpour, R. (2025). Inter- Annotator Agreement and Its Reflection in LLMs and Responsible AI. FLAIRS 2025.
Toliyat, A., Levitan, S. I., Peng, Z., & Etemadpour, R. (2022). Asian hate speech detection on Twitter during COVID-19. Frontiers in Artificial Intelligence, 5.
Chun, A., Li, A., Toliyat, A., Geller, J. (2020). Tracking Citizen’s Concerns during COVID-19 Pandemic. In Proceedings of the 21st Annual International Conference on Digital Government Research (dg.o '20). Association for Computing Machinery, New York, NY, USA, 322–323.

Research Interests

Research interests include artificial intelligence, machine learning, natural language processing, large language models, Responsible AI, and AI-enabled decision-support systems. Current work focuses on developing and evaluating technically substantive computational methods and frameworks for reliable, responsible, and reproducible AI, including model evaluation, evidence fidelity, robustness, error analysis, and human-AI decision-making. These methods are applied across domains such as healthcare, public health, organizational decision-making, cybersecurity, transportation, and other data-intensive settings.

Teaching

Student-centered, applied teaching that integrates information systems, programming, databases, data analytics, artificial intelligence and machine learning, cybersecurity, and decision-support systems. Courses emphasize hands-on technical work, real-world datasets and scenarios, and the interpretation of analytical results so that students can translate computing and AI methods into well-supported organizational and operational decisions.

Current teaching includes BUS 215 – Information Management.

Courses and Areas of Expertise

Information Systems and Information Management; Artificial Intelligence, Machine Learning, Natural Language Processing, and Large Language Models; Data Science and Business Analytics; Database Systems; Python and C++ Programming; Data Structures and Algorithms; Computer Networks; Cybersecurity; Responsible AI; and AI-Enabled Decision-Support Systems.

Student Mentoring and Recognition

Mentors undergraduate and graduate students in technically substantive research involving artificial intelligence, machine learning, natural language processing, large language models, Responsible AI, and related computational methods. Student collaborations emphasize reproducible research, methodological rigor, and the development of work suitable for peer-reviewed publication. A coauthored student paper on securing CPDLC communications received 2nd Place Best Student Paper at ICNS 2026.

Research Collaboration Opportunities

I welcome research collaborations with doctoral, master’s, and advanced undergraduate students, as well as independent researchers, interested in technically substantive work involving artificial intelligence, machine learning, natural language processing, large language models, Responsible AI, and AI-enabled decision support. I am particularly interested in collaborations that combine methodological or computational innovation with meaningful applications across different domains. Research projects emphasize rigorous, reproducible work with the goal of developing high-quality scholarship suitable for peer-reviewed journal publication. Students and researchers with strong Python, machine learning, NLP, data analysis, or prior scholarly research experience are especially encouraged to contact me.